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EEGminer: discovering interpretable features of brain activity with learnable filters.

Siegfried LudwigStylianos BakasDimitrios A AdamosNikolaos LaskarisYannis PanagakisStefanos Zafeiriou
Published in: Journal of neural engineering (2024)
The proposed method offers strong interpretability of learned features while reaching similar levels of accuracy achieved by black box deep learning models. This improved trustworthiness may promote the use of deep learning models in real world applications. The model code is available at https://github.com/SMLudwig/EEGminer/.
Keyphrases
  • deep learning
  • artificial intelligence
  • convolutional neural network
  • machine learning
  • transcription factor